Assimilation of surface soil moisture has been the focus of many hydrologic data assimilation studies due to the profound influence soil moisture has in controlling the exchange of water and energy between the land surface and the atmosphere. Spatially distributed observations of subsurface soil moisture, however, are not typically available and data assimilation of surface soil moisture has been used in many studies to generate improvements in the root zone. Land surface models differ significantly in their representation of subsurface soil moisture processes and as a result, differ in their ability to propagate information from the assimilated surface soil moisture into the deeper soil layers. In this study, we evaluate the impact of land surface model representations in the assimilation of surface soil moisture. The study is conducted using the recently developed Land Information System (LIS) data assimilation testbed, which is an interoperable framework for sequential data assimilation by enabling the integrated use of multiple land surface models, multiple observations and multiple data assimilation algorithms. A suite of Observing System Simulation Experiments (OSSEs) is conducted using the Noah and Catchment land surface models and using the Ensemble Kalman Filter (EnKF) algorithms for the assimilation of surface soil moisture. These experiments illustrate the sensitivity of model parameterizations and physical representations on the efficiency of soil moisture assimilation.